Psychological well-being and coping strategies of elderly people during the COVID-19 pandemic in Hungary

Abstract

Objective

During COVID-19 lockdown the enforced social isolation and other pandemic-related changes highly increased the risk of mental health problems. We aimed to discover how elderly people coped with the psychological burdens of pandemic and the social isolation in Hungary.

Methods

This study included 589 (441 females) Hungarian individuals, aged 60–83 (M = 68.1, SD = 4.46). We collected online survey data to reach a wide population of elderly. Results of hierarchical linear modelling and structural equation modelling (SEM) analyses established how the current life-changing circumstances, the intolerance of uncertainty, loneliness and social support influence the mental health (e.g. depression, anxiety, well-being) of the elderly. The model was used to explore how adaptive and maladaptive emotion regulation strategies mediated the effects.

Results

Findings showed that perceived change in mood, social connectedness, and quality of life was negatively affected by catastrophizing and loneliness; whereas positive refocusing and contamination fear had a positive effect. According to the SEM analysis, intolerance of uncertainty and loneliness directly affected mental health. Further, maladaptive emotion regulation strategies mediated the connection between intolerance of uncertainty, contamination fear, loneliness and mental health. Whereas adaptive emotion regulation strategy mediated the connection between social support from friends, contamination fear, loneliness and mental health.

Conclusion

Overall, our research might help the understanding of how external and internal factors contributed to the well-being of elderly people during the COVID-19. The model can also be translated into professional interventions to develop coping strategies among elderly for the challenges of COVID-19 pandemic in their lives.

Keywords:

• Aging
• coronavirus
• social isolation
• mental health
• structural equation modelling

Acknowledgements

Special thanks should be given to Senior Academy Program (Institute of Transdisciplinary Discoveries, Medical School, University of Pecs and Semmelweis University, Budapest) for their valuable support and for their help in collecting data.

Disclosure statement

All authors declare that they have no conflicts of interest.

Current author contribution statement

Conceptualization: BL, NA, AS, ANZ, TB; Methodology: BL, NA, OI, DS, TB Formal analysis and investigation: ANZ; Writing - original draft preparation: BL, NA, OI, ANZ; Writing - review and editing: BL, NA, ANZ; Funding acquisition: BL, NA, ANZ; Supervision: BL, ANZ

Notes

1 In fact, the post hoc computed achieved power was 0.995070 in this study.

2 To reduce the number of variables in the SEM model, and thus, make it more reliable, we used linear regression modelling (enter method) to explore which of the measured factors of each questionnaire (CERQ and MSPSS) predict mental health of participants during the COVID lockdown.Supplementary material 1 shows the procedure of selecting the variables and exact statistical results. Based on the results we only used the scales CERQ positive refocusing, CERQ catastrophizing, CERQ rumination, and MSPSS friends from their respective questionnaires in the SEM analysis.

Additional information

Funding

The project has been supported by the European Union, co-financed by the European Social Fund. Comprehensive Development for Implementing Smart Specialization Strategies at the University of Pécs. Grant number: EFOP-3.6.1.-16-2016-00004.